Chance Constrained Optimization in a Home Energy Management System

Huang, Yantai; Wang, Lei; Guo, Weian; Kang, Qi; Wu, Qidi

doi:10.1109/tsg.2016.2550031

Cited by 158 publications

(72 citation statements)

References 23 publications

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“…Yantai Huang et al in [19] offered a DR scheme to optimize the operation of the home appliances in the Home Energy Management System (HEMS). To reduce the computational load burden, Two Point Estimation Method (2PEM)-embedded PSO-based approach was proposed.…”

Section: Related Workmentioning

confidence: 99%

Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes

et al. 2017

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Abstract:The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting.

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Section: Related Workmentioning

confidence: 99%

Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes

et al. 2017

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“…The power output of the photovoltaic generation with 10 kWh capacity and the power consumption of uncontrollable loads are taken from [19], as shown in Figure 1a. The real-time price for buying electricity is easily taken from [14], shown in Figure 1b, and the price for selling electricity is assumed to be a fixed tariff, being 0.34 yuan/kWh. The operational parameters of interruptible loads, uninterruptible loads, and the energy storage device are listed in Tables 1-3. Taken from [13] and [27], the heat resistance and capacity for the air conditioner are 18 • C/kW and 0.525 kWh/ • C, and the mass capacity of the tank is 100 L. Additionally, the rated power of the air conditioner and water heater are 1.8 kW and 3.6 kW, respectively.…”

Section: Simulation Designmentioning

confidence: 99%

“…In [13], typical uncertain parameters in the day-ahead temperature scheduling for air-conditioning are modeled by membership functions with fuzzy set theory. In [14], normally distributed random variables are used to describe the uncertain parameters, and a chance constrained optimization model is then formulated to accommodate the uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization for Household Load Scheduling with Uncertain Parameters

Wang

Fang

et al. 2018

Applied Sciences

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Home energy management systems (HEMS) face many challenges of uncertainty, which have a great impact on the scheduling of home appliances. To handle the uncertain parameters in the household load scheduling problem, this paper uses a robust optimization method to rebuild the household load scheduling model for home energy management. The model proposed in this paper can provide the complete robust schedules for customers while considering the disturbance of uncertain parameters. The complete robust schedules can not only guarantee the customers' comfort constraints but also cooperatively schedule the electric devices for cost minimization and load shifting. Moreover, it is available for customers to obtain multiple schedules through setting different robust levels while considering the trade-off between the comfort and economy.

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“…Furthermore, the authors introduced the improved optimization algorithm to solve the DEM problem. In [15], a chance constraint model has been presented to optimize the performance of the domestic devices. The improved particle swarm optimization method has been used to optimize the problem based on the proposed demand response program.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Flexibility Management Based on Predictive Dispatch Model of Domestic Energy Management System

et al. 2017

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This paper proposes a predictive dispatch model to manage energy flexibility in the domestic energy system. Electric Vehicles (EV), batteries and shiftable loads are devices that provide energy flexibility in the proposed system. The proposed energy management problem consists of two stages: day-ahead and real time. A hybrid method is defined for the first time in this paper to model the uncertainty of the PV power generation based on its power prediction. In the day-ahead stage, the uncertainty is modeled by interval bands. On the other hand, the uncertainty of PV power generation is modeled through a stochastic scenario-based method in the real-time stage. The performance of the proposed hybrid Interval-Stochastic (InterStoch) method is compared with the Modified Stochastic Predicted Band (MSPB) method. Moreover, the impacts of energy flexibility and the demand response program on the expected profit and transacted electrical energy of the system are assessed in the case study presented in this paper.

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Chance Constrained Optimization in a Home Energy Management System

Cited by 158 publications

References 23 publications

Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes

Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes

Robust Optimization for Household Load Scheduling with Uncertain Parameters

Energy Flexibility Management Based on Predictive Dispatch Model of Domestic Energy Management System

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